Assessing human factors during simulation: The development and preliminary validation of the rescue assessment tool
Bibliographic record
Abstract
Background: Failure to rescue the deteriorating patient is a concern for all healthcare providers. In response to this problem providers have introduced a range of interventions to promote timely rescue. Human factors and non-technical skills play a part in both the recognition of ill patients and in the delivery of interventions associated with their successful rescue. Given the risks to patient safety which failure to rescue raises, simulation provides a vehicle for staff training and development in terms of both technical and non-technical skills. This paper describes the development and preliminary validation of a human factors rating tool specifically designed to assess the non-technical skills associated with the recognition and rescue of the deteriorating patient. Methods: Using high fidelity simulation scenarios related to patient deterioration Faculty independently rated student performance. Scoring took place using video footage of the students’ performance. Data were analyzed to establish the validity of the tool, internal consistency between categories and elements and inter-rater reliability. Results: Content validity was established through a process of review and by checking for duplicate or redundant items. The internal consistency of the tool was acceptable with a Cronbach’s alpha of 0.84. Factor analysis suggested that the tool assessed only two components rather than the three hypothesized during tool development. The components were labelled as recognizing and responding and leading and reassuring. Inter-rater reliability was initially poor at 0.21 but following training of raters this rose to above 0.8 for two videos related to the same scenario one which had been used during training. However, when the scenario changed the reliability dropped to 0.5. Conclusions: Rescue appears to be a well-structured tool with good levels of inter-rater reliability following intensive training related to the specific scenario being scored. Further work is required to establish all aspects of construct validity and to ensure test-retest reliability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.035 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".